Cognitive computing is a promising technology for deriving intelligence and knowledge from huge volumes of data. Today's cognitive computers are usually based on the Von Neumann architecture in which the computing and the memory units are separated. Cognitive computing is inherently data-centric, meaning that huge amounts of data need to be shuttled back and forth at high speeds. As the Von Neumann architecture is rather inefficient for such a task, it is becoming increasingly clear that other architectures are desired to build efficient cognitive computers, in particular architectures where memory and logic coexist in some form.
Memcomputing is a key non-Von Neumann approach being researched. A key element in this novel computing paradigm is a high-density, low-power, variable state, programmable and non-volatile memory device.
A fundamental computational primitive is a matrix-vector multiplication. This primitive is of particular interest as it forms the basis of several linear algebraic operations and it is one of the most commonly used mathematical operations in science and engineering. A matrix is usually represented by a two-dimensional array of matrix elements and a vector by a one-dimensional array of vector elements. A matrix may be considered as array of vectors. Hence a matrix-vector multiplication can be generalized to a matrix-matrix multiplication and to a vector-vector multiplication.
However, many challenges remain to be solved in order to perform accurate matrix-vector computations in an array of memory devices. One of them is inherent device conductance variations over time which can come from temperature changes, temporal drift or read disturb. Such conductance variations may lead to systematic errors in the multiplication results.
Accordingly, there is a need for new and improved memcomputing devices, in particular for memcomputing devices that can perform matrix-vector multiplications.